Operation of Cells and Multi-Cells and Cell Clusters
For purposes herein, a “cell” is used to describe a geographic area serviced by a set of (number of) transmit antennas. Such antennas can reside on one or more base-stations. They may be co-located at one point, or geographically spread over the cell. They may not even physically reside in the geographic boundary of the cell, though it is often the case they are physically located in the cell. For downlink traffic, this set of antennas services user terminals in the cell by jointly transmitting signals to such users. The transmitted signals are produced and controlled by a single common physical layer mechanism. That is, within a given “cell” the transmit antennas are coordinated. Such a common physical layer mechanism can be implemented by a number of processing entities that may be spread over a number of base stations, but which are jointly controlled to achieve a common result. The mechanism may also be implemented by a single processing entity. A classic example of such “cells” is shown in FIG. 1. Here the antennas are collocated at a base-station. Referring to FIG. 1, user terminals within classic hexagonally shaped cells are mapped to the base-station site that is “geographically closest” (e.g., the center of the call). This closest mapping results in the classic hexagonal pattern. For example, Cell 1 consists of a central set of 4 antennas at a single base-station, BS1, supporting a group of users including but not limited to user1, user2, user3, and user4. This “geographically closest” station rule makes sense in a model in which the received signal energy a user gets from any base-station (or antenna) decreases monotonically with the distance from that station, and by the same mathematical function for every station. In general, with shadowing and other effects, the cell boundaries will not conform to such a regular structure. In this classic multi-cell scenario, which has a fixed classic cell structure mapping users on a one-to-one basis with base-stations, a single base-station transmits only to users assigned to its cell.
It is well known that if neighboring base-stations use the same transmission resource (e.g., send signals on the same frequency band at the same time) that users in a cell will experience interference from other cells. Such “inter-cell interference” (ICI) can be quite extreme near the edges of cells, thus limiting performance for users in such areas. This is a classic problem with any cell structure. For example, user4 and user5 in FIG. 1 will experience high interference levels relative to their received signal levels. In contrast, users such as user1 and user2 may see less interference and less effect from a multi-cell environment. Nonetheless, all users do experience ICI.
A scheduling algorithm, such as a proportionally fair scheduling algorithm, can be used to ensure that users such as user1 and user2 are balanced fairly (or a desired fashion) with users seeing less favorable channel conditions such as user4 and user5. Without such a mechanism, if the system simply wanted to deliver the maximum per cell throughput, it would simply transmit only to users with the best channel conditions, severely penalizing other users such as user4 and user5.
To alleviate the detrimental effects of ICI, one can assume that cells coordinate their transmissions. One such way of doing so is to make sure neighboring base-stations do not use the same frequency at the same time. This results in the classic frequency reuse approach of a cellular system. Another way is to allow neighboring cells to use the same frequency at the same time, but to select signals for transmission that result in low ICI. One way to do so is to have a single underlying physical layer mechanism jointly control antennas from neighboring base-stations. In the extreme, one mechanism controls all transmit antennas over all stations, and the system reduces essentially to a single distributed antenna system with a common MIMO (downlink) broadcast (shared) channel. Here a user such as user4 in cell 1 is in fact serviced by signals coming from all antennas. In such an approach, ICI can be significantly reduced, even set to zero by Multi-user MIMO techniques such as Linear Zero-Forced Beamforming. In fact, in such an extreme the boundaries of “cells” have no meaning, and there are no longer uncoordinated signals crossing cell boundaries creating ICI. However, such a system is impractical.
A partial coordination may be more practical as shown in FIG. 2. Referring to FIG. 2, groups of cells (in this case three neighboring cells) can coordinate with each other. As illustrated, cells 1, 2 and 3 coordinate transmissions, cells 4, 5 and 6 coordinate transmissions, cells 7 and 8 coordinate, and cell 10 operates without coordinating with others. Each of these groups of three cells act as a single “cell”, or what is termed a “cluster”. If such coordination between base-stations is done correctly, users such as user4 and user5 can be made to see both less interference, and possibly better signal terms because they are no longer at a boundary with a neighboring cluster. They have now the option to use transmissions from up to 12 antennas over 3 remote locations. Thus, the system becomes more efficient. In addition, the scheduling of users in the cluster of cells 1, 2 and 3 can be done together.
However, because coordination is partial, such a system will inherently always have boundaries where users that see less favorable conditions. This is the case of users user6 and user7 in FIG. 2 which are at the boundary of two coordinating clusters.